We develop a Markov Chain Monte Carlo (MCMC) algorithm for estimating nested logit models in a Bayesian framework. Appropriate "heating target" and reparameterization techniques are adopted for fast mixing. For illustrative purposes, we have implemented the algorithm on two real-life examples...

This paper examines the determinants of inflation forecast uncertainty using a panel of density forecasts from the Survey of Professional Forecasters (SPF). Based on a dynamic heterogeneous panel data model, we find that the persistence in forecast uncertainty is much less than what the...

Using a standard decomposition of forecasts errors into common and idiosyncratic shocks, we show that aggregate forecast uncertainty can be expressed as the disagreement among the forecasters plus the perceived variability of future aggregate shocks. Thus, the reliability of disagreement as a...

Using data from the 1992 US National Survey of Veterans, we analyze Medicare-eligible veterans' use of VA and non-VA outpatient health care services. We apply a utility consistent, combined multinomial choice and count data model to identify factors that affect these veterans' outpatient health...

Abstract: We consider how to use information from reported density forecasts from surveys to identify asymmetry in forecasters' loss functions. We show that, for the three common loss functions - Lin-Lin, Linex, and Quad-Quad - we can infer the direction of loss asymmetry by just comparing point...

Based on 1992 US National Survey of Veterans, we analyzed the nature of veterans' inpatient and outpatient health care utilization by estimating a count data two-part hurdle model. We also identified factors that affect veterans' choices between VA and non- VA health care facilities, using a...

Using monthly GDP forecasts from Consensus Economics Inc. for 18 developed countries reported over 24 different forecast horizons during 1989-2004, we find that the survey forecasts do not have much value when the horizon goes beyond 18 months. Using two alternative approaches to measure the...

We have estimated a 4-step sequential probit model with and without sample separation information to characterize SSA's disability determination process. Under the program provisions, different criteria dictate the outcomes at different steps of the process. We used data on health, activity...

Using a Bayesian learning model with heterogeneity across agents, our study aims to identify the relative importance of alternative pathways through which professional forecasters disagree and reach consensus on the term structure of inflation and real GDP forecasts, resulting in different...

We compare the finite sample performance of a number of Bayesian and classical procedures for limited information simultaneous equations models with weak instruments by a Monte Carlo study. We consider recent Bayesian approaches developed by Chao and Phillips (1998, CP), Geweke (1996),...